{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T03:33:08.353881Z",
     "start_time": "2020-06-15T03:33:08.338902Z"
    }
   },
   "source": [
    "# 集成算法原理：\n",
    "TODO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T03:07:22.229513Z",
     "start_time": "2020-06-15T03:07:22.003921Z"
    }
   },
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.metrics import precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T03:09:09.837781Z",
     "start_time": "2020-06-15T03:09:09.830999Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 4) 3\n"
     ]
    }
   ],
   "source": [
    "iris = datasets.load_iris()\n",
    "data = iris.data\n",
    "label = iris.target\n",
    "\n",
    "print(data.shape, len(set(label)))\n",
    "# 150 个样本，4个特征，3个类别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T03:10:07.997733Z",
     "start_time": "2020-06-15T03:10:07.989810Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   sepal_l  sepal_w  petal_l  petal_w\n",
      "0      5.1      3.5      1.4      0.2\n",
      "1      4.9      3.0      1.4      0.2\n",
      "2      4.7      3.2      1.3      0.2\n",
      "3      4.6      3.1      1.5      0.2\n",
      "4      5.0      3.6      1.4      0.2\n"
     ]
    }
   ],
   "source": [
    "########### 便于特征工程 ########################\n",
    "data1 = pd.DataFrame(data)\n",
    "data1.columns = ['sepal_l', 'sepal_w', 'petal_l', 'petal_w']\n",
    "print(data1.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T03:10:34.519066Z",
     "start_time": "2020-06-15T03:10:34.512369Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   label\n",
      "0      0\n",
      "1      0\n",
      "2      0\n",
      "3      0\n",
      "4      0\n"
     ]
    }
   ],
   "source": [
    "label1 = pd.DataFrame(label)\n",
    "label1.columns = ['label']\n",
    "print(label1.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T03:27:27.449230Z",
     "start_time": "2020-06-15T03:27:27.442828Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集长度： 105 \n",
      " 测试集长度： 45\n"
     ]
    }
   ],
   "source": [
    "########### 拆分数据集 ##############################\n",
    "train_x, test_x, train_y, test_y = train_test_split(\n",
    "    data1.values,\n",
    "    label1.values, # 数组形状：105,1\n",
    "    test_size=0.3,\n",
    "    random_state=42,\n",
    ")\n",
    "print(\"训练集长度：\", len(train_x), '\\n', \"测试集长度：\", len(test_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T03:16:22.924290Z",
     "start_time": "2020-06-15T03:15:53.584204Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://mirrors.aliyun.com/pypi/simple/\n",
      "Collecting xgboost\n",
      "  Downloading https://mirrors.aliyun.com/pypi/packages/7c/32/a11befbb003e0e6b7e062a77f010dfcec0ec3589be537b02d2eb2ff93b9a/xgboost-1.1.1-py3-none-manylinux2010_x86_64.whl (127.6 MB)\n",
      "\u001b[K     |████████████████████████████████| 127.6 MB 11.0 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: scipy in /home/yangbin7/anaconda3/lib/python3.7/site-packages (from xgboost) (1.4.1)\n",
      "Requirement already satisfied: numpy in /home/yangbin7/anaconda3/lib/python3.7/site-packages (from xgboost) (1.18.1)\n",
      "Installing collected packages: xgboost\n",
      "Successfully installed xgboost-1.1.1\n"
     ]
    }
   ],
   "source": [
    "!pip install xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T03:17:11.678745Z",
     "start_time": "2020-06-15T03:17:11.614724Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[11:17:11] WARNING: /workspace/src/learner.cc:480: \n",
      "Parameters: { silent } might not be used.\n",
      "\n",
      "  This may not be accurate due to some parameters are only used in language bindings but\n",
      "  passed down to XGBoost core.  Or some parameters are not used but slip through this\n",
      "  verification. Please open an issue if you find above cases.\n",
      "\n",
      "\n",
      "[[0.00650657 0.96226174 0.03123166]\n",
      " [0.970643   0.02533227 0.00402478]\n",
      " [0.0033913  0.00692109 0.9896876 ]\n",
      " [0.00654362 0.9677424  0.02571394]\n",
      " [0.00615641 0.9104776  0.08336602]]\n"
     ]
    }
   ],
   "source": [
    "########## xgboost 原生接口 ########################\n",
    "import xgboost as xgb\n",
    "\n",
    "# 验证集，数据需要转换成 DMatrix 对象\n",
    "test_data = xgb.DMatrix(test_x, label=test_y)\n",
    "\n",
    "# 超参数\n",
    "xgb_params = {\n",
    "    'eta': 0.3,                        # 学习率\n",
    "    'silent': True,                    # 输出运行信息\n",
    "    'objective': 'multi:softprob',     # 使用多分类生成概率矩阵格式\n",
    "    'num_class': 3,                    # 类别数\n",
    "    'max_depth': 3                     # 深度\n",
    "}\n",
    "num_rounds = 20                        # 训练轮次\n",
    "\n",
    "# 训练模型，输入数据 DMatrix 对象\n",
    "model = xgb.train(xgb_params, xgb.DMatrix(train_x, label=train_y), num_rounds)\n",
    "\n",
    "# 模型预测\n",
    "test_pred = model.predict(test_data)\n",
    "print(test_pred[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T03:17:40.187742Z",
     "start_time": "2020-06-15T03:17:40.180437Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "预测结果： [1 0 2 1 1 0 1 2 1 1 2 0 0 0 0 1 2 1 1 2 0 2 0 2 2 2 2 2 0 0 0 0 1 0 0 2 1\n",
      " 0 0 0 2 1 1 0 0]\n",
      "验证集准确率： 1.0\n",
      "验证集召回率： 1.0\n"
     ]
    }
   ],
   "source": [
    "########## 模型性能 #########################################\n",
    "y_hat = np.asarray([np.argmax(row) for row in test_pred])\n",
    "print('预测结果：', y_hat)\n",
    "print(\"验证集准确率：\", precision_score(test_y, y_hat, average='macro'))\n",
    "print(\"验证集召回率：\", recall_score(test_y, y_hat, average='macro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LightGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T03:18:48.669888Z",
     "start_time": "2020-06-15T03:18:47.252892Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking in indexes: https://mirrors.aliyun.com/pypi/simple/\n",
      "Collecting lightgbm\n",
      "  Downloading https://mirrors.aliyun.com/pypi/packages/0b/9d/ddcb2f43aca194987f1a99e27edf41cf9bc39ea750c3371c2a62698c509a/lightgbm-2.3.1-py2.py3-none-manylinux1_x86_64.whl (1.2 MB)\n",
      "\u001b[K     |████████████████████████████████| 1.2 MB 3.0 MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied: scikit-learn in /home/yangbin7/anaconda3/lib/python3.7/site-packages (from lightgbm) (0.22.1)\n",
      "Requirement already satisfied: scipy in /home/yangbin7/anaconda3/lib/python3.7/site-packages (from lightgbm) (1.4.1)\n",
      "Requirement already satisfied: numpy in /home/yangbin7/anaconda3/lib/python3.7/site-packages (from lightgbm) (1.18.1)\n",
      "Requirement already satisfied: joblib>=0.11 in /home/yangbin7/anaconda3/lib/python3.7/site-packages (from scikit-learn->lightgbm) (0.15.1)\n",
      "Installing collected packages: lightgbm\n",
      "Successfully installed lightgbm-2.3.1\n"
     ]
    }
   ],
   "source": [
    "!pip install lightgbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T03:30:02.994511Z",
     "start_time": "2020-06-15T03:30:02.986436Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集长度: 105\n",
      "测试集长度: 45\n"
     ]
    }
   ],
   "source": [
    "# 划分训练集和测试集\n",
    "train_x, test_x, train_y, test_y = train_test_split(\n",
    "    data1,\n",
    "    label1,\n",
    "    test_size=0.3,\n",
    "    random_state=42,\n",
    ")\n",
    "print(\"训练集长度:\", len(train_x))\n",
    "print(\"测试集长度:\", len(test_x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T03:30:25.448058Z",
     "start_time": "2020-06-15T03:30:25.413502Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\ttraining's multi_error: 0.133333\tvalid_1's multi_error: 0.155556\n",
      "[2]\ttraining's multi_error: 0.0571429\tvalid_1's multi_error: 0.0444444\n",
      "[3]\ttraining's multi_error: 0.0666667\tvalid_1's multi_error: 0\n",
      "[4]\ttraining's multi_error: 0.0666667\tvalid_1's multi_error: 0\n",
      "[5]\ttraining's multi_error: 0.0666667\tvalid_1's multi_error: 0\n",
      "[6]\ttraining's multi_error: 0.0666667\tvalid_1's multi_error: 0\n",
      "[7]\ttraining's multi_error: 0.0761905\tvalid_1's multi_error: 0\n",
      "[8]\ttraining's multi_error: 0.0761905\tvalid_1's multi_error: 0\n",
      "[9]\ttraining's multi_error: 0.0761905\tvalid_1's multi_error: 0\n",
      "[10]\ttraining's multi_error: 0.0666667\tvalid_1's multi_error: 0\n",
      "[[0.13683286 0.63500393 0.22816321]\n",
      " [0.69436834 0.15467706 0.15095461]\n",
      " [0.12934308 0.16125127 0.70940565]\n",
      " [0.14172417 0.62195656 0.23631927]\n",
      " [0.13683286 0.63500393 0.22816321]]\n",
      "test的预测结果: [1 0 2 1 1 0 1 2 1 1 2 0 0 0 0 1 2 1 1 2 0 2 0 2 2 2 2 2 0 0 0 0 1 0 0 2 1\n",
      " 0 0 0 2 1 1 0 0]\n",
      "验证集精准率： 1.0\n",
      "验证集召回率： 1.0\n"
     ]
    }
   ],
   "source": [
    "########## lightgbm 原生接口 ########################\n",
    "import lightgbm as lgb\n",
    "\n",
    "# 数据转换成模块要求的格式\n",
    "train_data = lgb.Dataset(train_x, train_y)\n",
    "test_data = lgb.Dataset(test_x, test_y)\n",
    "\n",
    "# 设置参数\n",
    "lgb_params = {\n",
    "    'boosting_type': 'gbdt',\n",
    "    'objective': 'multiclass',\n",
    "    'metric': 'multi_error',\n",
    "    'verbose': 1,  # <0 显示致命的, =0 显示错误 (警告), >0 显示信息\n",
    "    'num_class': 3  # 类别数\n",
    "}\n",
    "\n",
    "# 模型训练\n",
    "clf = lgb.train(\n",
    "    lgb_params,\n",
    "    train_data,\n",
    "    num_boost_round=10,  # 训练轮次\n",
    "    valid_sets=[train_data, test_data],\n",
    "    verbose_eval=1,\n",
    ")\n",
    "# 模型预测\n",
    "test_pre = clf.predict(\n",
    "    test_x,\n",
    "    num_iteration=clf.best_iteration,  # 最佳的模型\n",
    ")\n",
    "# print(test_pre)\n",
    "print(test_pre[:5])\n",
    "\n",
    "# 选择表示最高概率的列\n",
    "test_pre_1 = np.asarray([np.argmax(row) for row in test_pre])\n",
    "print(\"test的预测结果:\", test_pre_1)\n",
    "\n",
    "# 模型评估\n",
    "print('验证集精准率：', precision_score(test_y, test_pre_1, average='macro'))\n",
    "print('验证集召回率：', recall_score(test_y, test_pre_1, average='macro'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T03:32:34.898744Z",
     "start_time": "2020-06-15T03:32:34.852031Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 0 2 1 1 0 1 2 1 1 2 0 0 0 0 1 2 1 1 2 0 2 0 2 2 2 2 2 0 0 0 0 1 0 0 2 1\n",
      " 0 0 0 2 1 1 0 0]\n",
      "验证集精准率： 1.0\n",
      "验证集召回率： 1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yangbin7/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_label.py:235: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/home/yangbin7/anaconda3/lib/python3.7/site-packages/sklearn/preprocessing/_label.py:268: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "############## sklearn 接口： ########################################\n",
    "\n",
    "from lightgbm import LGBMClassifier\n",
    "\n",
    "# 模型参数：\n",
    "lgb_params = {\n",
    "    'learning_rate': 0.1,\n",
    "    'max_bin': 150,             # 分箱操作\n",
    "    'num_leaves': 32,           # 叶子树   \n",
    "    'max_depth': 11,            # 最大深度\n",
    "    'objective': 'multiclass',\n",
    "    'n_estimators': 300         # 迭代次数\n",
    "}\n",
    "model = LGBMClassifier(**lgb_params)\n",
    "\n",
    "# Numpy 数组格式的数据直接输入\n",
    "model.fit(train_x, train_y)\n",
    "\n",
    "# 预测，输出预测结果\n",
    "y_hat = model.predict(test_x)\n",
    "print(y_hat)\n",
    "\n",
    "# 模型评估\n",
    "print('验证集精准率：', precision_score(test_y, y_hat, average='macro'))\n",
    "print('验证集召回率：', recall_score(test_y, y_hat, average='macro'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> - lgb.train 中正则化参数为 `lambda_l1, lambda_l2`，sklearn 中对应为 `reg_alpha,reg_lambda`\n",
    "- 多分类时 `lgb.train` 除了 `objective:multiclass` 外，还要指定 `num_class:5`，而 sklearn 则只需要指定`objective:multiclass`\n",
    "- 迭代次数在sklearn 中是 `n_estimators:20` ，在模型初始化时指定"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 银行客服分类\n",
    "「银行营销数据集(Bank Marketing Data Set)」，「分类目标」是预测客户是(' 1 ')或者否(' 0 ')购买该银行的产品。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T05:12:10.046901Z",
     "start_time": "2020-06-15T05:12:10.041524Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as bp\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import precision_score, recall_score\n",
    "import lightgbm as lgb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T05:12:32.399039Z",
     "start_time": "2020-06-15T05:12:32.363076Z"
    }
   },
   "outputs": [],
   "source": [
    "path = '../datasets/'\n",
    "train = pd.read_csv(path + 'bank_train_set.csv')\n",
    "test = pd.read_csv(path + 'bank_test_set.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T05:12:54.596168Z",
     "start_time": "2020-06-15T05:12:54.583295Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 25317 entries, 0 to 25316\n",
      "Data columns (total 18 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   ID         25317 non-null  int64 \n",
      " 1   age        25317 non-null  int64 \n",
      " 2   job        25317 non-null  object\n",
      " 3   marital    25317 non-null  object\n",
      " 4   education  25317 non-null  object\n",
      " 5   default    25317 non-null  object\n",
      " 6   balance    25317 non-null  int64 \n",
      " 7   housing    25317 non-null  object\n",
      " 8   loan       25317 non-null  object\n",
      " 9   contact    25317 non-null  object\n",
      " 10  day        25317 non-null  int64 \n",
      " 11  month      25317 non-null  object\n",
      " 12  duration   25317 non-null  int64 \n",
      " 13  campaign   25317 non-null  int64 \n",
      " 14  pdays      25317 non-null  int64 \n",
      " 15  previous   25317 non-null  int64 \n",
      " 16  poutcome   25317 non-null  object\n",
      " 17  y          25317 non-null  int64 \n",
      "dtypes: int64(9), object(9)\n",
      "memory usage: 3.5+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T05:13:23.062520Z",
     "start_time": "2020-06-15T05:13:23.052046Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10852 entries, 0 to 10851\n",
      "Data columns (total 17 columns):\n",
      " #   Column     Non-Null Count  Dtype \n",
      "---  ------     --------------  ----- \n",
      " 0   ID         10852 non-null  int64 \n",
      " 1   age        10852 non-null  int64 \n",
      " 2   job        10852 non-null  object\n",
      " 3   marital    10852 non-null  object\n",
      " 4   education  10852 non-null  object\n",
      " 5   default    10852 non-null  object\n",
      " 6   balance    10852 non-null  int64 \n",
      " 7   housing    10852 non-null  object\n",
      " 8   loan       10852 non-null  object\n",
      " 9   contact    10852 non-null  object\n",
      " 10  day        10852 non-null  int64 \n",
      " 11  month      10852 non-null  object\n",
      " 12  duration   10852 non-null  int64 \n",
      " 13  campaign   10852 non-null  int64 \n",
      " 14  pdays      10852 non-null  int64 \n",
      " 15  previous   10852 non-null  int64 \n",
      " 16  poutcome   10852 non-null  object\n",
      "dtypes: int64(8), object(9)\n",
      "memory usage: 1.4+ MB\n"
     ]
    }
   ],
   "source": [
    "test.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T05:17:21.884947Z",
     "start_time": "2020-06-15T05:17:21.858529Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>ID</th>\n",
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       "      <th>day</th>\n",
       "      <th>duration</th>\n",
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       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>25317.000000</td>\n",
       "      <td>25317.000000</td>\n",
       "      <td>25317.000000</td>\n",
       "      <td>25317.000000</td>\n",
       "      <td>25317.000000</td>\n",
       "      <td>25317.000000</td>\n",
       "      <td>25317.000000</td>\n",
       "      <td>25317.000000</td>\n",
       "      <td>25317.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>12659.000000</td>\n",
       "      <td>40.935379</td>\n",
       "      <td>1357.555082</td>\n",
       "      <td>15.835289</td>\n",
       "      <td>257.732393</td>\n",
       "      <td>2.772050</td>\n",
       "      <td>40.248766</td>\n",
       "      <td>0.591737</td>\n",
       "      <td>0.116957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7308.532719</td>\n",
       "      <td>10.634289</td>\n",
       "      <td>2999.822811</td>\n",
       "      <td>8.319480</td>\n",
       "      <td>256.975151</td>\n",
       "      <td>3.136097</td>\n",
       "      <td>100.213541</td>\n",
       "      <td>2.568313</td>\n",
       "      <td>0.321375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>-8019.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6330.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>103.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12659.000000</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>448.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18988.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>1435.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>317.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>25317.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>102127.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>3881.000000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>854.000000</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 ID           age        balance           day      duration  \\\n",
       "count  25317.000000  25317.000000   25317.000000  25317.000000  25317.000000   \n",
       "mean   12659.000000     40.935379    1357.555082     15.835289    257.732393   \n",
       "std     7308.532719     10.634289    2999.822811      8.319480    256.975151   \n",
       "min        1.000000     18.000000   -8019.000000      1.000000      0.000000   \n",
       "25%     6330.000000     33.000000      73.000000      8.000000    103.000000   \n",
       "50%    12659.000000     39.000000     448.000000     16.000000    181.000000   \n",
       "75%    18988.000000     48.000000    1435.000000     21.000000    317.000000   \n",
       "max    25317.000000     95.000000  102127.000000     31.000000   3881.000000   \n",
       "\n",
       "           campaign         pdays      previous             y  \n",
       "count  25317.000000  25317.000000  25317.000000  25317.000000  \n",
       "mean       2.772050     40.248766      0.591737      0.116957  \n",
       "std        3.136097    100.213541      2.568313      0.321375  \n",
       "min        1.000000     -1.000000      0.000000      0.000000  \n",
       "25%        1.000000     -1.000000      0.000000      0.000000  \n",
       "50%        2.000000     -1.000000      0.000000      0.000000  \n",
       "75%        3.000000     -1.000000      0.000000      0.000000  \n",
       "max       55.000000    854.000000    275.000000      1.000000  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##### 查看数值特征的统计特征，均值、方差、最小、最大等\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T05:18:39.701940Z",
     "start_time": "2020-06-15T05:18:39.688104Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>age</th>\n",
       "      <th>balance</th>\n",
       "      <th>day</th>\n",
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       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.008465</td>\n",
       "      <td>0.032719</td>\n",
       "      <td>-0.020171</td>\n",
       "      <td>0.229149</td>\n",
       "      <td>-0.038265</td>\n",
       "      <td>0.065807</td>\n",
       "      <td>0.047028</td>\n",
       "      <td>0.556627</td>\n",
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       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>0.008465</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>-0.016070</td>\n",
       "      <td>0.000416</td>\n",
       "      <td>0.006171</td>\n",
       "      <td>-0.026431</td>\n",
       "      <td>0.006575</td>\n",
       "      <td>0.029916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>balance</th>\n",
       "      <td>0.032719</td>\n",
       "      <td>0.093740</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010245</td>\n",
       "      <td>0.026042</td>\n",
       "      <td>-0.010419</td>\n",
       "      <td>0.001032</td>\n",
       "      <td>0.015792</td>\n",
       "      <td>0.057564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>day</th>\n",
       "      <td>-0.020171</td>\n",
       "      <td>-0.016070</td>\n",
       "      <td>0.010245</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.031946</td>\n",
       "      <td>0.168830</td>\n",
       "      <td>-0.092892</td>\n",
       "      <td>-0.050706</td>\n",
       "      <td>-0.031886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duration</th>\n",
       "      <td>0.229149</td>\n",
       "      <td>0.000416</td>\n",
       "      <td>0.026042</td>\n",
       "      <td>-0.031946</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.087780</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>0.001315</td>\n",
       "      <td>0.394746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>campaign</th>\n",
       "      <td>-0.038265</td>\n",
       "      <td>0.006171</td>\n",
       "      <td>-0.010419</td>\n",
       "      <td>0.168830</td>\n",
       "      <td>-0.087780</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.089224</td>\n",
       "      <td>-0.031667</td>\n",
       "      <td>-0.075173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdays</th>\n",
       "      <td>0.065807</td>\n",
       "      <td>-0.026431</td>\n",
       "      <td>0.001032</td>\n",
       "      <td>-0.092892</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>-0.089224</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.411688</td>\n",
       "      <td>0.107565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>previous</th>\n",
       "      <td>0.047028</td>\n",
       "      <td>0.006575</td>\n",
       "      <td>0.015792</td>\n",
       "      <td>-0.050706</td>\n",
       "      <td>0.001315</td>\n",
       "      <td>-0.031667</td>\n",
       "      <td>0.411688</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.088337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>0.556627</td>\n",
       "      <td>0.029916</td>\n",
       "      <td>0.057564</td>\n",
       "      <td>-0.031886</td>\n",
       "      <td>0.394746</td>\n",
       "      <td>-0.075173</td>\n",
       "      <td>0.107565</td>\n",
       "      <td>0.088337</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                ID       age   balance       day  duration  campaign  \\\n",
       "ID        1.000000  0.008465  0.032719 -0.020171  0.229149 -0.038265   \n",
       "age       0.008465  1.000000  0.093740 -0.016070  0.000416  0.006171   \n",
       "balance   0.032719  0.093740  1.000000  0.010245  0.026042 -0.010419   \n",
       "day      -0.020171 -0.016070  0.010245  1.000000 -0.031946  0.168830   \n",
       "duration  0.229149  0.000416  0.026042 -0.031946  1.000000 -0.087780   \n",
       "campaign -0.038265  0.006171 -0.010419  0.168830 -0.087780  1.000000   \n",
       "pdays     0.065807 -0.026431  0.001032 -0.092892  0.000040 -0.089224   \n",
       "previous  0.047028  0.006575  0.015792 -0.050706  0.001315 -0.031667   \n",
       "y         0.556627  0.029916  0.057564 -0.031886  0.394746 -0.075173   \n",
       "\n",
       "             pdays  previous         y  \n",
       "ID        0.065807  0.047028  0.556627  \n",
       "age      -0.026431  0.006575  0.029916  \n",
       "balance   0.001032  0.015792  0.057564  \n",
       "day      -0.092892 -0.050706 -0.031886  \n",
       "duration  0.000040  0.001315  0.394746  \n",
       "campaign -0.089224 -0.031667 -0.075173  \n",
       "pdays     1.000000  0.411688  0.107565  \n",
       "previous  0.411688  1.000000  0.088337  \n",
       "y         0.107565  0.088337  1.000000  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##### 查看不同数值特征之间的相关性\n",
    "train.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T05:20:04.280630Z",
     "start_time": "2020-06-15T05:20:04.272924Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "blue-collar      5456\n",
       "management       5296\n",
       "technician       4241\n",
       "admin.           2909\n",
       "services         2342\n",
       "retired          1273\n",
       "self-employed     884\n",
       "entrepreneur      856\n",
       "unemployed        701\n",
       "housemaid         663\n",
       "student           533\n",
       "unknown           163\n",
       "Name: job, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#### 查看特定特征的取值分布\n",
    "train.job.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T05:20:47.804168Z",
     "start_time": "2020-06-15T05:20:47.793539Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    22356\n",
       "1     2961\n",
       "Name: y, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##### 数据的类别分布\n",
    "train.y.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> - 数据集中，需要注意不同的数据格式，`object，int64`等\n",
    "- 测试数据集中没有目标那一列，为了便于特征工程，通常**将训练数据与测试数据合并**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T05:23:48.046260Z",
     "start_time": "2020-06-15T05:23:48.023064Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <td>no</td>\n",
       "      <td>291</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>9</td>\n",
       "      <td>may</td>\n",
       "      <td>150</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>technician</td>\n",
       "      <td>divorced</td>\n",
       "      <td>primary</td>\n",
       "      <td>no</td>\n",
       "      <td>5076</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>7</td>\n",
       "      <td>apr</td>\n",
       "      <td>99</td>\n",
       "      <td>1</td>\n",
       "      <td>251</td>\n",
       "      <td>2</td>\n",
       "      <td>other</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>47</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>104</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>14</td>\n",
       "      <td>jul</td>\n",
       "      <td>77</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>management</td>\n",
       "      <td>single</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>-994</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>18</td>\n",
       "      <td>jul</td>\n",
       "      <td>174</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>42</td>\n",
       "      <td>technician</td>\n",
       "      <td>divorced</td>\n",
       "      <td>secondary</td>\n",
       "      <td>no</td>\n",
       "      <td>2974</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>unknown</td>\n",
       "      <td>21</td>\n",
       "      <td>may</td>\n",
       "      <td>187</td>\n",
       "      <td>5</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "      <td>unknown</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  age         job   marital  education default  balance housing loan  \\\n",
       "0   1   43  management   married   tertiary      no      291     yes   no   \n",
       "1   2   42  technician  divorced    primary      no     5076     yes   no   \n",
       "2   3   47      admin.   married  secondary      no      104     yes  yes   \n",
       "3   4   28  management    single  secondary      no     -994     yes  yes   \n",
       "4   5   42  technician  divorced  secondary      no     2974     yes   no   \n",
       "\n",
       "    contact  day month  duration  campaign  pdays  previous poutcome  y  \n",
       "0   unknown    9   may       150         2     -1         0  unknown  0  \n",
       "1  cellular    7   apr        99         1    251         2    other  0  \n",
       "2  cellular   14   jul        77         2     -1         0  unknown  0  \n",
       "3  cellular   18   jul       174         2     -1         0  unknown  0  \n",
       "4   unknown   21   may       187         5     -1         0  unknown  0  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "####### 将训练数据与测试数据合并\n",
    "test[\"y\"] = -1\n",
    "data = train.append(test).reset_index(drop=True) # 重排索引\n",
    "# data = pd.concat([train,test]).reset_index(drop=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T05:25:45.002902Z",
     "start_time": "2020-06-15T05:25:44.863250Z"
    }
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "from numba import jit\n",
    "from tqdm import tqdm\n",
    "from tqdm import tqdm_notebook\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# about sklearn\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.model_selection import StratifiedKFold, KFold, RepeatedKFold\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import StandardScaler as std\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from sklearn.metrics import f1_score\n",
    "#about time\n",
    "import time\n",
    "import datetime\n",
    "from datetime import datetime, timedelta\n",
    "\n",
    "#Garbage collection\n",
    "import gc\n",
    "# scipy\n",
    "from scipy.signal import hilbert\n",
    "from scipy.signal import hann\n",
    "from scipy.signal import convolve\n",
    "from scipy import stats\n",
    "import scipy.spatial.distance as dist\n",
    "#other\n",
    "from collections import Counter\n",
    "from statistics import mode\n",
    "#warning\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import json\n",
    "import math\n",
    "from itertools import product\n",
    "import ast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T06:31:48.476667Z",
     "start_time": "2020-06-15T06:31:48.371231Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "aaca498c6916475092aee6f713745ec6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=9.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "############ 非数值特征编码\n",
    "\n",
    "cat_col = [\n",
    "    i for i in data.select_dtypes(object).columns if i not in ['ID', 'y']\n",
    "]\n",
    "for i in tqdm_notebook(cat_col):\n",
    "    lbl = LabelEncoder()\n",
    "    # 转化为类别\n",
    "    data[i] = lbl.fit_transform(data[i].astype(str))\n",
    "    # 增加新特征，计算每种取值的数量\n",
    "    data['count_' + i] = data.groupby([i])[i].transform('count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T06:31:57.189383Z",
     "start_time": "2020-06-15T06:31:57.178089Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>balance</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>...</th>\n",
       "      <th>y</th>\n",
       "      <th>count_job</th>\n",
       "      <th>count_marital</th>\n",
       "      <th>count_education</th>\n",
       "      <th>count_default</th>\n",
       "      <th>count_housing</th>\n",
       "      <th>count_loan</th>\n",
       "      <th>count_contact</th>\n",
       "      <th>count_month</th>\n",
       "      <th>count_poutcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>291</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>7595</td>\n",
       "      <td>21857</td>\n",
       "      <td>10654</td>\n",
       "      <td>35524</td>\n",
       "      <td>19959</td>\n",
       "      <td>30363</td>\n",
       "      <td>10393</td>\n",
       "      <td>10957</td>\n",
       "      <td>29554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5076</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>6083</td>\n",
       "      <td>4140</td>\n",
       "      <td>5493</td>\n",
       "      <td>35524</td>\n",
       "      <td>19959</td>\n",
       "      <td>30363</td>\n",
       "      <td>23437</td>\n",
       "      <td>2365</td>\n",
       "      <td>1475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>47</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>104</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>4124</td>\n",
       "      <td>21857</td>\n",
       "      <td>18509</td>\n",
       "      <td>35524</td>\n",
       "      <td>19959</td>\n",
       "      <td>5806</td>\n",
       "      <td>23437</td>\n",
       "      <td>5569</td>\n",
       "      <td>29554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-994</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>7595</td>\n",
       "      <td>10172</td>\n",
       "      <td>18509</td>\n",
       "      <td>35524</td>\n",
       "      <td>19959</td>\n",
       "      <td>5806</td>\n",
       "      <td>23437</td>\n",
       "      <td>5569</td>\n",
       "      <td>29554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>42</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2974</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>6083</td>\n",
       "      <td>4140</td>\n",
       "      <td>18509</td>\n",
       "      <td>35524</td>\n",
       "      <td>19959</td>\n",
       "      <td>30363</td>\n",
       "      <td>10393</td>\n",
       "      <td>10957</td>\n",
       "      <td>29554</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  age  job  marital  education  default  balance  housing  loan  contact  \\\n",
       "0   1   43    4        1          2        0      291        1     0        2   \n",
       "1   2   42    9        0          0        0     5076        1     0        0   \n",
       "2   3   47    0        1          1        0      104        1     1        0   \n",
       "3   4   28    4        2          1        0     -994        1     1        0   \n",
       "4   5   42    9        0          1        0     2974        1     0        2   \n",
       "\n",
       "   ...  y  count_job  count_marital  count_education  count_default  \\\n",
       "0  ...  0       7595          21857            10654          35524   \n",
       "1  ...  0       6083           4140             5493          35524   \n",
       "2  ...  0       4124          21857            18509          35524   \n",
       "3  ...  0       7595          10172            18509          35524   \n",
       "4  ...  0       6083           4140            18509          35524   \n",
       "\n",
       "   count_housing  count_loan  count_contact  count_month  count_poutcome  \n",
       "0          19959       30363          10393        10957           29554  \n",
       "1          19959       30363          23437         2365            1475  \n",
       "2          19959        5806          23437         5569           29554  \n",
       "3          19959        5806          23437         5569           29554  \n",
       "4          19959       30363          10393        10957           29554  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T06:33:07.431432Z",
     "start_time": "2020-06-15T06:33:07.426120Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['age', 'job', 'marital', 'education', 'default', 'balance', 'housing', 'loan', 'contact', 'day', 'month', 'duration', 'campaign', 'pdays', 'previous', 'poutcome', 'count_job', 'count_marital', 'count_education', 'count_default', 'count_housing', 'count_loan', 'count_contact', 'count_month', 'count_poutcome']\n"
     ]
    }
   ],
   "source": [
    "feats = [col for col in data.columns if col not in ['y', 'ID']]\n",
    "print(feats)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 分类模型"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 最基础模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T06:43:29.817644Z",
     "start_time": "2020-06-15T06:43:29.614538Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\tvalid_0's auc: 0.882792\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[2]\tvalid_0's auc: 0.900334\n",
      "[3]\tvalid_0's auc: 0.907919\n",
      "[4]\tvalid_0's auc: 0.919039\n",
      "[5]\tvalid_0's auc: 0.922838\n",
      "[6]\tvalid_0's auc: 0.923926\n",
      "[7]\tvalid_0's auc: 0.924013\n",
      "[8]\tvalid_0's auc: 0.926266\n",
      "[9]\tvalid_0's auc: 0.92802\n",
      "[10]\tvalid_0's auc: 0.927792\n",
      "[11]\tvalid_0's auc: 0.929748\n",
      "[12]\tvalid_0's auc: 0.929966\n",
      "[13]\tvalid_0's auc: 0.930611\n",
      "[14]\tvalid_0's auc: 0.931156\n",
      "[15]\tvalid_0's auc: 0.931509\n",
      "[16]\tvalid_0's auc: 0.931247\n",
      "[17]\tvalid_0's auc: 0.933151\n",
      "[18]\tvalid_0's auc: 0.933398\n",
      "[19]\tvalid_0's auc: 0.933606\n",
      "[20]\tvalid_0's auc: 0.934439\n",
      "[21]\tvalid_0's auc: 0.934698\n",
      "[22]\tvalid_0's auc: 0.934954\n",
      "[23]\tvalid_0's auc: 0.934926\n",
      "[24]\tvalid_0's auc: 0.934756\n",
      "[25]\tvalid_0's auc: 0.934782\n",
      "[26]\tvalid_0's auc: 0.934728\n",
      "[27]\tvalid_0's auc: 0.935008\n",
      "[28]\tvalid_0's auc: 0.935103\n",
      "[29]\tvalid_0's auc: 0.935133\n",
      "[30]\tvalid_0's auc: 0.935262\n",
      "[31]\tvalid_0's auc: 0.935066\n",
      "[32]\tvalid_0's auc: 0.935316\n",
      "[33]\tvalid_0's auc: 0.93493\n",
      "[34]\tvalid_0's auc: 0.934977\n",
      "[35]\tvalid_0's auc: 0.934916\n",
      "[36]\tvalid_0's auc: 0.934886\n",
      "[37]\tvalid_0's auc: 0.934678\n",
      "[38]\tvalid_0's auc: 0.93448\n",
      "[39]\tvalid_0's auc: 0.934282\n",
      "[40]\tvalid_0's auc: 0.934269\n",
      "[41]\tvalid_0's auc: 0.934097\n",
      "[42]\tvalid_0's auc: 0.934267\n",
      "[43]\tvalid_0's auc: 0.934064\n",
      "[44]\tvalid_0's auc: 0.933922\n",
      "[45]\tvalid_0's auc: 0.934114\n",
      "[46]\tvalid_0's auc: 0.934188\n",
      "[47]\tvalid_0's auc: 0.934354\n",
      "[48]\tvalid_0's auc: 0.934259\n",
      "[49]\tvalid_0's auc: 0.93411\n",
      "[50]\tvalid_0's auc: 0.933924\n",
      "[51]\tvalid_0's auc: 0.933952\n",
      "[52]\tvalid_0's auc: 0.933293\n",
      "[53]\tvalid_0's auc: 0.933242\n",
      "[54]\tvalid_0's auc: 0.933195\n",
      "[55]\tvalid_0's auc: 0.933022\n",
      "[56]\tvalid_0's auc: 0.933038\n",
      "[57]\tvalid_0's auc: 0.933143\n",
      "[58]\tvalid_0's auc: 0.933083\n",
      "[59]\tvalid_0's auc: 0.93305\n",
      "[60]\tvalid_0's auc: 0.93289\n",
      "[61]\tvalid_0's auc: 0.932709\n",
      "[62]\tvalid_0's auc: 0.932668\n",
      "[63]\tvalid_0's auc: 0.932477\n",
      "[64]\tvalid_0's auc: 0.932488\n",
      "[65]\tvalid_0's auc: 0.932376\n",
      "[66]\tvalid_0's auc: 0.9324\n",
      "[67]\tvalid_0's auc: 0.932447\n",
      "[68]\tvalid_0's auc: 0.932303\n",
      "[69]\tvalid_0's auc: 0.93234\n",
      "[70]\tvalid_0's auc: 0.932401\n",
      "[71]\tvalid_0's auc: 0.932236\n",
      "[72]\tvalid_0's auc: 0.932202\n",
      "[73]\tvalid_0's auc: 0.932107\n",
      "[74]\tvalid_0's auc: 0.932122\n",
      "[75]\tvalid_0's auc: 0.932021\n",
      "[76]\tvalid_0's auc: 0.931992\n",
      "[77]\tvalid_0's auc: 0.931823\n",
      "[78]\tvalid_0's auc: 0.931779\n",
      "[79]\tvalid_0's auc: 0.93162\n",
      "[80]\tvalid_0's auc: 0.931713\n",
      "[81]\tvalid_0's auc: 0.931663\n",
      "[82]\tvalid_0's auc: 0.931574\n",
      "[83]\tvalid_0's auc: 0.93155\n",
      "[84]\tvalid_0's auc: 0.931282\n",
      "[85]\tvalid_0's auc: 0.931062\n",
      "[86]\tvalid_0's auc: 0.93094\n",
      "[87]\tvalid_0's auc: 0.930849\n",
      "[88]\tvalid_0's auc: 0.930699\n",
      "[89]\tvalid_0's auc: 0.930479\n",
      "[90]\tvalid_0's auc: 0.930501\n",
      "[91]\tvalid_0's auc: 0.930302\n",
      "[92]\tvalid_0's auc: 0.930297\n",
      "[93]\tvalid_0's auc: 0.930459\n",
      "[94]\tvalid_0's auc: 0.930394\n",
      "[95]\tvalid_0's auc: 0.93047\n",
      "[96]\tvalid_0's auc: 0.930368\n",
      "[97]\tvalid_0's auc: 0.930217\n",
      "[98]\tvalid_0's auc: 0.930047\n",
      "[99]\tvalid_0's auc: 0.929854\n",
      "[100]\tvalid_0's auc: 0.92992\n",
      "[101]\tvalid_0's auc: 0.929967\n",
      "[102]\tvalid_0's auc: 0.930063\n",
      "[103]\tvalid_0's auc: 0.929837\n",
      "[104]\tvalid_0's auc: 0.929859\n",
      "[105]\tvalid_0's auc: 0.930093\n",
      "[106]\tvalid_0's auc: 0.930137\n",
      "[107]\tvalid_0's auc: 0.929838\n",
      "[108]\tvalid_0's auc: 0.92962\n",
      "[109]\tvalid_0's auc: 0.929548\n",
      "[110]\tvalid_0's auc: 0.929554\n",
      "[111]\tvalid_0's auc: 0.92958\n",
      "[112]\tvalid_0's auc: 0.929556\n",
      "[113]\tvalid_0's auc: 0.929524\n",
      "[114]\tvalid_0's auc: 0.929563\n",
      "[115]\tvalid_0's auc: 0.929362\n",
      "[116]\tvalid_0's auc: 0.929362\n",
      "[117]\tvalid_0's auc: 0.929373\n",
      "[118]\tvalid_0's auc: 0.92941\n",
      "[119]\tvalid_0's auc: 0.929233\n",
      "[120]\tvalid_0's auc: 0.92914\n",
      "[121]\tvalid_0's auc: 0.92905\n",
      "[122]\tvalid_0's auc: 0.928995\n",
      "[123]\tvalid_0's auc: 0.929066\n",
      "[124]\tvalid_0's auc: 0.928918\n",
      "[125]\tvalid_0's auc: 0.92884\n",
      "[126]\tvalid_0's auc: 0.928739\n",
      "[127]\tvalid_0's auc: 0.928817\n",
      "[128]\tvalid_0's auc: 0.928882\n",
      "[129]\tvalid_0's auc: 0.928892\n",
      "[130]\tvalid_0's auc: 0.92887\n",
      "[131]\tvalid_0's auc: 0.928818\n",
      "[132]\tvalid_0's auc: 0.928591\n",
      "Early stopping, best iteration is:\n",
      "[32]\tvalid_0's auc: 0.935316\n"
     ]
    }
   ],
   "source": [
    "model = lgb.LGBMClassifier(\n",
    "    boosting_type='gbdt',\n",
    "    num_leaves=30,\n",
    "    reg_alpha=0,\n",
    "    reg_lambda=0,\n",
    "    n_estimators=1500,\n",
    "    objective='binary',\n",
    "    metric=\"auc\",\n",
    "    subsample=0.95,\n",
    "    colsample_bytree=0.7,\n",
    "    subsample_freq=1,\n",
    "    learning_rate=0.2,\n",
    "    random_state=2020,\n",
    ")\n",
    "\n",
    "train_x = data[data['y'] != -1][feats]\n",
    "train_y = data[data['y'] != -1]['y']\n",
    "test_x = data[data['y'] == -1][feats]\n",
    "\n",
    "train_x, val_x, train_y, val_y = train_test_split(\n",
    "    train_x,\n",
    "    train_y,\n",
    "    test_size=0.3,\n",
    "    random_state=42,\n",
    ")\n",
    "\n",
    "model.fit(\n",
    "    train_x,\n",
    "    train_y,\n",
    "    eval_set=[(val_x, val_y)],\n",
    "    eval_metric=\"auc\",\n",
    "    early_stopping_rounds=100,\n",
    ")\n",
    "test_pred = model.predict_proba(test_x)[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T06:43:38.120118Z",
     "start_time": "2020-06-15T06:43:38.112964Z"
    }
   },
   "outputs": [],
   "source": [
    "pred = data[data['y'] == -1][['ID']]\n",
    "pred['pred'] = test_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T06:44:07.714584Z",
     "start_time": "2020-06-15T06:44:07.707033Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>pred</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25317</th>\n",
       "      <td>25318</td>\n",
       "      <td>0.040618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25318</th>\n",
       "      <td>25319</td>\n",
       "      <td>0.018166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25319</th>\n",
       "      <td>25320</td>\n",
       "      <td>0.004880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25320</th>\n",
       "      <td>25321</td>\n",
       "      <td>0.599200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25321</th>\n",
       "      <td>25322</td>\n",
       "      <td>0.058148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          ID      pred\n",
       "25317  25318  0.040618\n",
       "25318  25319  0.018166\n",
       "25319  25320  0.004880\n",
       "25320  25321  0.599200\n",
       "25321  25322  0.058148"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 交叉验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-06-15T06:54:20.378522Z",
     "start_time": "2020-06-15T06:53:53.644644Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\ttraining's auc: 0.770676\tvalid_1's auc: 0.766114\n",
      "[2]\ttraining's auc: 0.793711\tvalid_1's auc: 0.790743\n",
      "[3]\ttraining's auc: 0.801404\tvalid_1's auc: 0.784552\n",
      "[4]\ttraining's auc: 0.92015\tvalid_1's auc: 0.907589\n",
      "[5]\ttraining's auc: 0.930803\tvalid_1's auc: 0.917609\n",
      "[6]\ttraining's auc: 0.934361\tvalid_1's auc: 0.918405\n",
      "[7]\ttraining's auc: 0.938241\tvalid_1's auc: 0.923064\n",
      "[8]\ttraining's auc: 0.940171\tvalid_1's auc: 0.925207\n",
      "[9]\ttraining's auc: 0.941514\tvalid_1's auc: 0.927533\n",
      "[10]\ttraining's auc: 0.943292\tvalid_1's auc: 0.929315\n",
      "[11]\ttraining's auc: 0.944095\tvalid_1's auc: 0.928978\n",
      "[12]\ttraining's auc: 0.945465\tvalid_1's auc: 0.931375\n",
      "[13]\ttraining's auc: 0.946381\tvalid_1's auc: 0.931256\n",
      "[14]\ttraining's auc: 0.94774\tvalid_1's auc: 0.93204\n",
      "[15]\ttraining's auc: 0.948457\tvalid_1's auc: 0.932856\n",
      "[16]\ttraining's auc: 0.949524\tvalid_1's auc: 0.933375\n",
      "[17]\ttraining's auc: 0.950195\tvalid_1's auc: 0.932777\n",
      "[18]\ttraining's auc: 0.951011\tvalid_1's auc: 0.9327\n",
      "[19]\ttraining's auc: 0.952026\tvalid_1's auc: 0.932634\n",
      "[20]\ttraining's auc: 0.953111\tvalid_1's auc: 0.933336\n",
      "[21]\ttraining's auc: 0.953739\tvalid_1's auc: 0.933024\n",
      "[22]\ttraining's auc: 0.954458\tvalid_1's auc: 0.933321\n",
      "[23]\ttraining's auc: 0.9552\tvalid_1's auc: 0.933169\n",
      "[24]\ttraining's auc: 0.956235\tvalid_1's auc: 0.933441\n",
      "[25]\ttraining's auc: 0.956856\tvalid_1's auc: 0.933109\n",
      "[26]\ttraining's auc: 0.957502\tvalid_1's auc: 0.933207\n",
      "[27]\ttraining's auc: 0.958125\tvalid_1's auc: 0.932844\n",
      "[28]\ttraining's auc: 0.958605\tvalid_1's auc: 0.933\n",
      "[29]\ttraining's auc: 0.959238\tvalid_1's auc: 0.932961\n",
      "[30]\ttraining's auc: 0.959941\tvalid_1's auc: 0.933565\n",
      "[31]\ttraining's auc: 0.960513\tvalid_1's auc: 0.933404\n",
      "[32]\ttraining's auc: 0.961199\tvalid_1's auc: 0.933388\n",
      "[33]\ttraining's auc: 0.961668\tvalid_1's auc: 0.933644\n",
      "[34]\ttraining's auc: 0.96241\tvalid_1's auc: 0.93335\n",
      "[35]\ttraining's auc: 0.96301\tvalid_1's auc: 0.933309\n",
      "[36]\ttraining's auc: 0.963571\tvalid_1's auc: 0.933187\n",
      "[37]\ttraining's auc: 0.964166\tvalid_1's auc: 0.933449\n",
      "[38]\ttraining's auc: 0.964569\tvalid_1's auc: 0.933231\n",
      "[39]\ttraining's auc: 0.965005\tvalid_1's auc: 0.933378\n",
      "[40]\ttraining's auc: 0.96576\tvalid_1's auc: 0.933366\n",
      "[41]\ttraining's auc: 0.96614\tvalid_1's auc: 0.932982\n",
      "[42]\ttraining's auc: 0.966716\tvalid_1's auc: 0.933347\n",
      "[43]\ttraining's auc: 0.967169\tvalid_1's auc: 0.933216\n",
      "[44]\ttraining's auc: 0.967697\tvalid_1's auc: 0.933162\n",
      "[45]\ttraining's auc: 0.967901\tvalid_1's auc: 0.933231\n",
      "[46]\ttraining's auc: 0.96848\tvalid_1's auc: 0.932954\n",
      "[47]\ttraining's auc: 0.96888\tvalid_1's auc: 0.932801\n",
      "[48]\ttraining's auc: 0.969274\tvalid_1's auc: 0.932923\n",
      "[49]\ttraining's auc: 0.969597\tvalid_1's auc: 0.93267\n",
      "[50]\ttraining's auc: 0.970024\tvalid_1's auc: 0.932408\n",
      "[51]\ttraining's auc: 0.970639\tvalid_1's auc: 0.931928\n",
      "[52]\ttraining's auc: 0.970994\tvalid_1's auc: 0.931881\n",
      "[53]\ttraining's auc: 0.971292\tvalid_1's auc: 0.931982\n",
      "[54]\ttraining's auc: 0.971752\tvalid_1's auc: 0.931954\n",
      "[55]\ttraining's auc: 0.972217\tvalid_1's auc: 0.931903\n",
      "[56]\ttraining's auc: 0.972615\tvalid_1's auc: 0.931564\n",
      "[57]\ttraining's auc: 0.972975\tvalid_1's auc: 0.93161\n",
      "[58]\ttraining's auc: 0.973275\tvalid_1's auc: 0.931629\n",
      "[59]\ttraining's auc: 0.973736\tvalid_1's auc: 0.931813\n",
      "[60]\ttraining's auc: 0.973953\tvalid_1's auc: 0.931857\n",
      "[61]\ttraining's auc: 0.974458\tvalid_1's auc: 0.931564\n",
      "[62]\ttraining's auc: 0.974902\tvalid_1's auc: 0.931792\n",
      "[63]\ttraining's auc: 0.975255\tvalid_1's auc: 0.931622\n",
      "[64]\ttraining's auc: 0.975608\tvalid_1's auc: 0.931713\n",
      "[65]\ttraining's auc: 0.975937\tvalid_1's auc: 0.931602\n",
      "[66]\ttraining's auc: 0.976232\tvalid_1's auc: 0.931872\n",
      "[67]\ttraining's auc: 0.976662\tvalid_1's auc: 0.931865\n",
      "[68]\ttraining's auc: 0.97706\tvalid_1's auc: 0.931865\n",
      "[69]\ttraining's auc: 0.977466\tvalid_1's auc: 0.931859\n",
      "[70]\ttraining's auc: 0.977717\tvalid_1's auc: 0.93182\n",
      "[71]\ttraining's auc: 0.978154\tvalid_1's auc: 0.931828\n",
      "[72]\ttraining's auc: 0.978433\tvalid_1's auc: 0.931766\n",
      "[73]\ttraining's auc: 0.978816\tvalid_1's auc: 0.931485\n",
      "[74]\ttraining's auc: 0.979075\tvalid_1's auc: 0.931669\n",
      "[75]\ttraining's auc: 0.979292\tvalid_1's auc: 0.931579\n",
      "[76]\ttraining's auc: 0.979562\tvalid_1's auc: 0.931348\n",
      "[77]\ttraining's auc: 0.979848\tvalid_1's auc: 0.931021\n",
      "[78]\ttraining's auc: 0.980073\tvalid_1's auc: 0.930782\n",
      "[79]\ttraining's auc: 0.980578\tvalid_1's auc: 0.930422\n",
      "[80]\ttraining's auc: 0.980725\tvalid_1's auc: 0.930485\n",
      "[81]\ttraining's auc: 0.981108\tvalid_1's auc: 0.930431\n",
      "[82]\ttraining's auc: 0.981507\tvalid_1's auc: 0.930618\n",
      "[83]\ttraining's auc: 0.981646\tvalid_1's auc: 0.930431\n",
      "[84]\ttraining's auc: 0.98179\tvalid_1's auc: 0.930585\n",
      "[85]\ttraining's auc: 0.982031\tvalid_1's auc: 0.930996\n",
      "[86]\ttraining's auc: 0.982242\tvalid_1's auc: 0.931015\n",
      "[87]\ttraining's auc: 0.982676\tvalid_1's auc: 0.930567\n",
      "[88]\ttraining's auc: 0.982883\tvalid_1's auc: 0.930696\n",
      "[89]\ttraining's auc: 0.983025\tvalid_1's auc: 0.93084\n",
      "[90]\ttraining's auc: 0.983314\tvalid_1's auc: 0.930684\n",
      "[91]\ttraining's auc: 0.983563\tvalid_1's auc: 0.930799\n",
      "[92]\ttraining's auc: 0.983892\tvalid_1's auc: 0.930937\n",
      "[93]\ttraining's auc: 0.984094\tvalid_1's auc: 0.930722\n",
      "[94]\ttraining's auc: 0.984482\tvalid_1's auc: 0.930598\n",
      "[95]\ttraining's auc: 0.984731\tvalid_1's auc: 0.930451\n",
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      "[98]\ttraining's auc: 0.985327\tvalid_1's auc: 0.930369\n",
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      "[103]\ttraining's auc: 0.986339\tvalid_1's auc: 0.929974\n",
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      "[105]\ttraining's auc: 0.986635\tvalid_1's auc: 0.929893\n",
      "[106]\ttraining's auc: 0.986816\tvalid_1's auc: 0.929803\n",
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      "[111]\ttraining's auc: 0.987622\tvalid_1's auc: 0.929472\n",
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      "[113]\ttraining's auc: 0.98796\tvalid_1's auc: 0.929108\n",
      "[114]\ttraining's auc: 0.988247\tvalid_1's auc: 0.928969\n",
      "[115]\ttraining's auc: 0.988424\tvalid_1's auc: 0.929\n",
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      "[118]\ttraining's auc: 0.988947\tvalid_1's auc: 0.928689\n",
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      "[123]\ttraining's auc: 0.989742\tvalid_1's auc: 0.92806\n",
      "[124]\ttraining's auc: 0.989955\tvalid_1's auc: 0.928199\n",
      "[125]\ttraining's auc: 0.990113\tvalid_1's auc: 0.92819\n",
      "[126]\ttraining's auc: 0.990211\tvalid_1's auc: 0.927881\n",
      "[127]\ttraining's auc: 0.990336\tvalid_1's auc: 0.927645\n",
      "[128]\ttraining's auc: 0.990421\tvalid_1's auc: 0.927567\n",
      "[129]\ttraining's auc: 0.990485\tvalid_1's auc: 0.927429\n",
      "[130]\ttraining's auc: 0.990616\tvalid_1's auc: 0.927508\n",
      "[131]\ttraining's auc: 0.99069\tvalid_1's auc: 0.927555\n",
      "[132]\ttraining's auc: 0.990801\tvalid_1's auc: 0.927632\n",
      "[133]\ttraining's auc: 0.990964\tvalid_1's auc: 0.927691\n",
      "[134]\ttraining's auc: 0.991143\tvalid_1's auc: 0.927937\n",
      "[135]\ttraining's auc: 0.991376\tvalid_1's auc: 0.928147\n",
      "[136]\ttraining's auc: 0.991483\tvalid_1's auc: 0.927779\n",
      "[137]\ttraining's auc: 0.991594\tvalid_1's auc: 0.927664\n",
      "[138]\ttraining's auc: 0.991738\tvalid_1's auc: 0.927465\n",
      "[139]\ttraining's auc: 0.99181\tvalid_1's auc: 0.92743\n",
      "[140]\ttraining's auc: 0.991944\tvalid_1's auc: 0.927346\n",
      "[141]\ttraining's auc: 0.992025\tvalid_1's auc: 0.927355\n",
      "[142]\ttraining's auc: 0.992135\tvalid_1's auc: 0.927467\n",
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    {
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     "text": [
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    {
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     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "output_type": "stream",
     "text": [
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     "text": [
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     "text": [
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    {
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     "output_type": "stream",
     "text": [
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     "text": [
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     ]
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    {
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     "output_type": "stream",
     "text": [
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     "text": [
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     "text": [
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    {
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     "text": [
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     "text": [
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     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "text": [
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     "text": [
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    {
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     "text": [
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    {
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     "text": [
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    {
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     "output_type": "stream",
     "text": [
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    {
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     "text": [
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    {
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     "output_type": "stream",
     "text": [
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     "text": [
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      "[1491]\ttraining's auc: 1\tvalid_1's auc: 0.919118\n",
      "[1492]\ttraining's auc: 1\tvalid_1's auc: 0.919234\n",
      "[1493]\ttraining's auc: 1\tvalid_1's auc: 0.919234\n",
      "[1494]\ttraining's auc: 1\tvalid_1's auc: 0.919178\n",
      "[1495]\ttraining's auc: 1\tvalid_1's auc: 0.919084\n",
      "[1496]\ttraining's auc: 1\tvalid_1's auc: 0.919129\n",
      "[1497]\ttraining's auc: 1\tvalid_1's auc: 0.919003\n",
      "[1498]\ttraining's auc: 1\tvalid_1's auc: 0.91897\n",
      "[1499]\ttraining's auc: 1\tvalid_1's auc: 0.918977\n",
      "[1500]\ttraining's auc: 1\tvalid_1's auc: 0.918951\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "n_splits = 10\n",
    "kfold = KFold(n_splits=10, shuffle=True, random_state=42)\n",
    "\n",
    "train_x = data[data['y'] != -1][feats]\n",
    "train_y = data[data['y'] != -1]['y']\n",
    "\n",
    "res = data[data['y'] == -1][['ID']]\n",
    "test_x = data[data['y'] == -1][feats]\n",
    "res['pred'] = 0\n",
    "\n",
    "for train_idx, val_idx in kfold.split(train_x):\n",
    "    model.random_state = model.random_state + 1\n",
    "\n",
    "    # 训练集索引，验证集索引\n",
    "    train_x1 = train_x.loc[train_idx]\n",
    "    train_y1 = train_y.loc[train_idx]\n",
    "\n",
    "    val_x1 = train_x.loc[val_idx]\n",
    "    val_y1 = train_y.loc[val_idx]\n",
    "\n",
    "    # 模型拟合\n",
    "    model.fit(train_x1,\n",
    "              train_y1,\n",
    "              eval_set=[(train_x1, train_y1), (val_x1, val_y1)])\n",
    "\n",
    "    # 模型预测\n",
    "    res['pred'] += model.predict_proba(test_x)[:, 1]\n",
    "\n",
    "res['pred'] = res['pred'] / 10\n",
    "\n",
    "## 结果保存到本地\n",
    "# res.to_csv('./submission10_fold.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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